MuTCR: Test Case Recommendation via Multi-Level Signature Matching
Off-the-shelf test cases provide developers with testing knowledge for their reference or reuse, which can help them reduce the effort of creating new test cases. Test case recommendation, a major way of achieving test case reuse, has been receiving the attention of researchers. The basic idea behind test case recommendation is that two similar test targets (methods under test) can reuse each other’s test cases. However, existing test case recommendation techniques either cannot be used in the cross-project scenario, or have low performance in terms of effectiveness and efficiency. In this paper, we propose a novel test case recommendation technique based on multi-level signature matching. The proposed multi-level signature matching consists of three matching strategies with different strict levels, including level-0 exact matching, level-1 fuzzy matching, and level-2 fuzzy matching. For the query test target given by the developer, level-0 exact matching helps to retrieve exact recommendations (test cases), while level-1 and level-2 fuzzy matching contribute to discovering richer relevant recommendations. We further develop a prototype called MuTCR for test case recommendation. We conduct comprehensive experiments to evaluate the effectiveness and efficiency of MuTCR. The experimental results demonstrate that compared with the state-of-the-art, MuTCR can recommend accurate test cases for more test targets. MuTCR is faster than the best baseline by three times based on the time cost. The user study is also performed to prove that the test cases recommended by MuTCR are useful in practice.
Tue 16 MayDisplayed time zone: Hobart change
13:45 - 15:15 | |||
13:45 22mTalk | Orchestration Strategies for Regression Test Suites AST 2023 Renan Greca Gran Sasso Science Institute, ISTI-CNR, Breno Miranda Federal University of Pernambuco, Antonia Bertolino National Research Council, Italy Pre-print | ||
14:07 22mTalk | Evaluating the Trade-offs of Text-based Diversity in Test Prioritization AST 2023 Ranim Khojah Chalmers | University of Gothenburg, Chi Hong Chao Chalmers | University of Gothenburg, Francisco Gomes de Oliveira Neto Chalmers University of Technology, Sweden / University of Gothenburg, Sweden | ||
14:30 22mTalk | MuTCR: Test Case Recommendation via Multi-Level Signature Matching AST 2023 Weisong Sun Nanjing University, Weidong Qian China Ship Scientific Research Center, Bin Luo Nanjing University, Zhenyu Chen Nanjing University | ||
14:52 22mTalk | Test Case Prioritization using Transfer Learning in Continuous Integration Environments AST 2023 Rezwana Mamata Ontario Tech University, Akramul Azim Ontario Tech University, Ramiro Liscano Ontario Tech University, Kevin Smith International Business Machines Corporation (IBM), Yee-Kang Chang International Business Machines Corporation (IBM), Gkerta Seferi International Business Machines Corporation (IBM), Qasim Tauseef International Business Machines Corporation (IBM) |